DOI: https://doi.org/10.21203/rs.3.rs-806304/v1
Identifying how socioeconomic positioning and genetic factors interact in the development of obesity is imperative for population-level obesity prevention strategies. The current study investigated whether social positioning, either independently or through interaction with a polygenic score for Body Mass Index (BMI-PGS), influences BMI trajectories across older adulthood.
Data were analysed from 7,183 individuals from the English Longitudinal Study of Aging (ELSA). Interactions between the BMI-PGS and; lower educational attainment, SSS, and income, on BMI trajectories across older adulthood were investigated through linear mixed effects models.
Lower educational attainment, SSS and income were each associated with a higher baseline BMI for women, but not for men. There were interaction effects between BMI-PGS and social positioning such that men aged > 65 with a lower educational attainment (β = 0.62; 95%CI = 0.00-1.24), men aged ≤ 65 of a lower income (β=-0.72, 95%CI=-1.21- -0.23) and women aged ≤ 65 of lower SSS (β=-1.41; 95%CI=-2.46-0.36) showed stronger associations between the BMI-PGS and baseline BMI.
Lower socioeconomic positioning showed adverse effects for women’s BMI in older adulthood. While the expression of the BMI-PGS, or extent to which it translates to a higher BMI, was subtly influenced by socioeconomic standing in both women and in men.
The prevalence of obesity, defined by a body mass index (BMI) ≥ 30, is associated with numerous adverse health implications in older age. These include an increased risk for diabetes1, cardiovascular diseases2, hypertension and even mortality.3 This relationship is anticipated to further increase as the general population continues to age, with associated costs of obesity being estimated to reach £49.9 billion per year by 2050.4
The distribution of obesity is unequal across socioeconomic positions (SEP)5, with rates being higher among those with lower education6, income7 or subjective social class.8 Obesity rates demonstrate a strong gradient in the UK as result of a greater exposure to the obesogenic environment and more limited opportunities for adequate nutrition and physical activity across the lifespan.9 While education, SSS and income are correlated, they reflect different aspects of SES at older ages.10 Education is typically completed in early life and shapes occupational trajectories; while income is an indicator of economic resources that influences opportunities for mobility, food choice, and access to exercise facilities relevant to adiposity in later life.11 Moreover, there is evidence to suggest that women’s BMI in adulthood appears to be more adversely affected by lower socioeconomic settings than in men.5 However, it is less well understood how BMI may be differentially influenced by specific markers of SEP between the sexes, potentially highlight more specific and fruitful targets for obesity prevention efforts in older adulthood.
Nonetheless, obesity has a strong genetic basis, with an estimated heritability ranging from ~ 40–70%.12 To uncover the molecular mechanisms underlying BMI, genome-wide association studies (GWAS) have been successful in identifying hundreds of significant loci associated with BMI, which together are shown to have a substantial additive influence on BMI.13 These GWAS have led to the development of the polygenic score approach, which represents an aggregate measure of polygenic risk for BMI by exploiting all loci associated with a higher BMI.14 Polygenic approaches also offer novel means to uncover the potentially modifying effect of the social environment on the expression of genetic risk towards a higher BMI. A more favourable socioeconomic environment may attenuate the expression of genetic risk for BMI, while a less favourable environment may enhance the expression of genetic risk. Hence, the same genetic risk may result in higher BMI for populations living in lower socioeconomic conditions; a so called gene-environment interaction (GxE).15 A recent study from Barcellos et al found a significant gene-by-education interaction within the large UK Biobank Sample.16 Moreover, Frank et al demonstrated the presence of GxE in relation to income, highlighting the potential for SEP measures outside of educational attainment to attenuate genetic risk towards BMI.17 However, the support for GxE in relation to obesity remains inconsistent.18–20 Moreover, GxE studies of BMI have primarily focused on educational attainment, and therefore alternative aspects of socioeconomic positioning remain unexplored.
The current study used a large population-representative cohort of older adults to investigate whether a higher genetic load of multiple risk alleles for BMI (a BMI-PGS score) was associated with BMI at baseline (~ 64 years of age) and rate of change in BMI over a 12-year follow-up period in older adults. We tested interactions between the BMI-PGS score and educational attainment, income, and SSS in relation to BMI at baseline and change in BMI over the follow-up period. We hypothesised that adults with a larger genetic susceptibility (BMI-PGS) would be at greater risk for accelerated increases in BMI over the 12-year period. Secondly, we hypothesised that the BMI-PGS would be more strongly associated with BMI at baseline and steeper BMI trajectories for individuals of a lower educational attainment, income or SSS; a GxE interaction.
Data were drawn from the English Longitudinal Study of Aging (ELSA), a nationally representative survey of English adults aged ≥ 50 years.21 Measures of socioeconomic position and covariates were taken from wave 2 (2004–2005) for the core sample (82%), or waves 4 (2008–2009) and 6 (2012–2013) for the respective refreshment samples (18%). Baseline measures of BMI were taken from wave 2 (2004–2005) for participants who provided blood samples for genotyping at wave 2 (77%), or wave 4 for those who provided blood samples at wave 4 (23%). Follow-up BMI measures were obtained from waves 6 (2012–2013) and 8 (2016–2017). Ethical approval for each ELSA wave was granted by the National Research Ethics Service (London Multicentre Research Ethics Committee). All participants gave informed consent and all experiments were performed in accordance with relevant guidelines and regulations.
Body Mass Index (BMI). BMI was calculated using standard formulae (weight in kilograms/height in square meters)22. Here, height and weight were measured during the nurse visit. Weight was measured using Tanita electronic scales to measure body weight without shoes and in light clothing. Height was determined by Stadiometer using the Frankfort plane on a ground level. At Wave 8, BMI was calculated using height measurements obtained from Wave 6, as no height measurements were obtained in 2016–2017.
Educational attainment. Educational Attainment was measured through self-reported highest educational qualification. Responses were derived into three categories: i) Higher Qualification (undergraduate or postgraduate degree level) ii) Secondary Qualification (A/O or GSCE level or equivalent) iii) Primary Qualification (Below A/O/GSCE or no qualification).
Subjective social status (SSS). SSS was measured through the MacArthur Scale of Subjective Social Status.23 This measure presents a drawing of a ladder with 10 rungs to represent where people stand in society, the higher up representing those with the most money, education, and jobs. Respondents were asked to place a single “X” on this ladder to rank their social standing, producing a score from 1–10, with 10 being the highest SSS. These raw scores were derived into tertiles: i) Top Tertile ii) Middle Tertile iii) Lowest Tertile of SSS.
Income. Income was measured through self-reported household equivalised income, adjusted for variation in household size. Income was calculated from detailed assessments of a full range of earned and unearned sources of income. The income variable was divided into tertile to represent the groups of individuals with i) High ii) Intermediate and iii) Low Tertiles of Income.
Covariates. Demographic covariates included marital status (not currently married, currently married). Behavioural covariates included smoking status (not-current smoker, current smoker) and physical activity level (sedentary or low activity at least once a week, moderate activity at least once a week, vigorous activity at least once a week), which was measured through self-reported intensity and frequency of physical activity.24 Participant responses were categorised according to their highest level of activity reported at least once a week. Health related covariates included the presence of a limiting longstanding illness (limiting illness reported, no limiting illness reported) and depressive symptomatology (current depressive symptoms, no current depressive symptoms). Depressive symptoms were measured with an 8-item version of the Centre for Epidemiologic Studies Depression Scale,25 which has comparable psychometric properties to the full 20-item scale; a score ≥ 4 was used to define participants with severe depressive symptoms.26. Lastly, genetic ancestry measured with principal components (see below), was included as a covariate to account for any ancestry differences in genetic structures that could bias our results.27
The genome-wide genotyping was performed at University College London Genomics in 2013–2014 using the Illumina HumanOmni2.5 BeadChips (HumanOmni2.5-4v1, HumanOmni2.5-8v1.3). Samples were removed based on call rate (< 0.99), suspected non-European ancestry, heterozygosity, and relatedness. Single Nucleotide polymorphisms (SNPs) were excluded if they were non-autosomal, the minor allele frequency was < 0.01%, if more than 2% of genotype data were missing and if the Hardy-Weinberg Equilibrium P < 10− 4. To investigate population structure, principal components analysis was conducted; top 10 principal components were retained to were used to adjust for possible population stratification in the association analyses.28,29
Polygenic score (PGS). To calculate PGS for BMI (BMI-PGS), we used summary statistics reported by the GIANT consortium (2018).13 To calculate BMI-PGS, BMI-associated SNPs, weighted by their effect size derived from the GIANT, were summed in a continuous score using PRSice. As previous research has highlighted that PGSs built from directly genotyped data either had more predictive power30 or did not differ significantly from PGSs calculated using imputed data,31 we calculated PGSs based on genotyped data at different P-value cut-offs. Because PGSs including all available SNPs either explain the most amount of variation in a trait or are not significantly different than PGSs based on different P-value thresholds, we utilised PGS that was based on a threshold of P-value of 1. A total of 798737 SNPs were included in BMI-PGS. To aid interpretability of the results, BMI-PGS was standardized to a mean of 0 (SD = 1).
To assess the interplay between BMI-PGS with socioeconomic position on BMI values at baseline and across the 12-year follow up period, we employed linear mixed effect models (LMMs) with maximum likelihood estimation.32 LMMs with maximum likelihood estimation maximise the use of longitudinal data, adjust for the correlation between repeated measures, weight estimates for missing data between waves, and increase statistical power and precision.32 Using Akaike Information Criterion and Bayesian Information Criterion, a quadratic model allowing for random intercepts and slopes was deemed most appropriate for our analyses. To test whether variation in BMI across SEP influenced the model results, heteroscedascity assumptions was examined, and where heteroscedascity was present, models used robust standard errors, using the vce(robust) command in STATA, relaxing the assumption that standard errors carry identical and equal distributions.33 Interactions between BMI-PGS and all three measures of socioeconomic position were investigated using multiplicative models. Each analysis was stratified by gender and age group (i.e., ≤ 65 years old vs > 65 years old). We used a significance level of 0.05 (two-tailed) for all analyses. All analyses were conducted in STATA release 16 (STATA Corp LP, USA).34
Sensitivity Analyses. In the sensitivity analyses we repeated all analyses as described above but with missing values imputed for both socioeconomic position (Educational attainment, SSS, and income) and all covariate measures using MissForest in RStudio version 3.6.2.6.35
The total sample consisted of 7183 ELSA participants for whom the quality-controlled genome-wide genotyping and BMI during the follow-up were available; of these 46% (N = 3304) were men and 54% (N = 3879) were women. The baseline mean age for men was 64.40 (standard deviation (SD) = 9.15) and for women was 64.35 (SD = 9.56). A larger proportion of men (74.88%) than women (56.17%) reported a longstanding illness (x2 = 6.11, P = 0.011); whereas a larger proportion of women (34.37%) than men (21.35%) showed elevated depressive symptoms (x2 = 148.59, P < 0.001). Men and women differed further in terms of marital status, level of physical activity, income, and educational attainment all reported at baseline (Table 1).
| Sample Characteristics | Men (n = 3304) | Women (n = 3878) | Test Statistics | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N(%) / Mean (SD) | N(%) / Mean (SD) | t / x2 | df | P value | |||||
| Age (years) | 64.40 (9.15) | 64.35 (9.56) | -0.31 | 7180 | 0.75 | ||||
| Source of Baseline BMI | |||||||||
| Wave 2 | 2461 (74.48) | 2937 (75.73) | 1.35 | 1 | 0.25 | ||||
| Wave 4 | 753 (22.79) | 841 (21.68) | |||||||
| Current Smoker | 512 (15.57) | 653 (16.89) | 2.26 | 1 | 0.13 | ||||
| Not married | 780 (23.61) | 1466 (37.79) | 167.06 | 1 | < 0.001 | ||||
| Income | |||||||||
| High | 1244 (38.71) | 1207 (32.15) | 49.81 | 2 | < 0.001 | ||||
| Moderate | 997 (31.02) | 1130 (30.10) | |||||||
| Low | 973 (30.27) | 1417 (37.75) | |||||||
| Highest Educational Attainment | |||||||||
| Higher qualification | 1117 (35.80) | 861 (25.04) | 90.02 | 2 | < 0.001 | ||||
| Secondary qualification | 812 (26.03) | 1040 (30.24) | |||||||
| Primary qualification | 1191 (38.17) | 1538 (44.72) | |||||||
| Subjective Social Status | |||||||||
| Top Tertile | 637 (20.33) | 585 (15.90) | 24.35 | 2 | < 0.001 | ||||
| Middle Tertile | 2215 (70.68) | 2780 (75.54) | |||||||
| Lower Tertile | 282 (9.00) | 315 (8.56) | |||||||
| Longstanding Illness present | 1758 (74.88) | 2177 (56.17) | 6.11 | 1 | 0.01 | ||||
| Poor Self-Reported Health | 1772 (23.37) | 1 938 (24.18) | 0.64 | 1 | 0.43 | ||||
| Physical Activity | |||||||||
| Sedentary | 532 (16.13) | 803 (20.75) | 35.63 | 2 | < 0.001 | ||||
| Moderate activity | 1578 (47.85) | 1878 (48.53) | |||||||
| Vigorous activity | 1188 (36.02) | 1189 (30.72) | |||||||
| Elevated Depressive Symptoms | 704 (21.35) | 1332 (34.37) | 148.59 | 1 | < 0.001 | ||||
| Body mass index | |||||||||
| Baselinea | 27.89 (4.27) | 27.97 ( 5.41) | -0.74 | 6990 | 0.45 | ||||
| Wave 6 | 28.11 (4.49) | 28.15 (5.60) | -0.23 | 4331 | 0.82 | ||||
| Wave 8 | 27.88 (4.44) | 27.76 (5.61) | 0.65 | 3212 | 0.51 | ||||
| a Combination of BMI measures collected at either wave 2 (for participants where blood was collected for genotyping at wave 2 (77%) and wave 4 (for participants where blood was collected at wave 4 (23%)) | |||||||||
As compared to the group with a higher qualification, having a primary qualification was associated with higher BMI at baseline for women aged ≤ 65 years old (β = 1.25; 95%CI = 0.64–1.85) (Table 2), women aged > 65 years old (β = 1.04; 95%CI = 0.35–1.72) and men aged > 65 years old (β = 0.52; 95%CI = 0.02–1.07) (Table 2). While having a secondary qualification was only associated with a higher BMI at baseline for women aged ≤ 65 years old (β = 1.02; 95%CI = 0.45–1.60). Regarding interaction effects, a 1-SD increase in BMI-PGS was associated with a higher baseline BMI of 0.62 points in men aged > 65 of a secondary education as compared to higher education (β = 0.62; 95% CI = 0.00-1.24) (Fig. 1). For rate of change in BMI, in men aged ≤ 65 years, a secondary (β = 0.06; 95%CI = 0.02–0.10) and primary qualification (β = 0.06; 95%CI = 0.01–0.11) was associated with a steeper increase in BMI across the 12-year follow up.
| ≤ 65 Years of Age | > 65 Years of Age | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Men | Women | Men | Women | ||||||||||
| β | 95% CI | β | 95% CI | β | 95% CI | β | 95% CI | ||||||
| Baseline | |||||||||||||
| PGS | 1.41*** | 1.10–1.71 | 1.69*** | 1.29–2.10 | 0.57** | 0.25–0.88 | 1.09*** | 0.59–1.59 | |||||
| Higher degree | - | - | - | - | - | - | - | - | |||||
| Secondary qualification | 0.33 | -0.13-0.82 | 1.02*** | 0.45–1.60 | 0.28 | -0.28 -0.86 | 0.39 | -0.40-1.20 | |||||
| Primary qualification | 0.42 | -0.08-0.93 | 1.25*** | 0.64–1.85 | 0.52* | 0.02–1.07* | 1.04** | 0.35–1.72 | |||||
| PGS × Higher qualification | - | - | - | - | - | - | - | - | |||||
| PGS × Secondary qualification | -0.34 | -0.81-0.12 | -0.17 | -0.73-0.37 | 0.62* | 0.09–1.16 | 0.56 | -0.40-1.20 | |||||
| PGS × Primary qualification | -0.19 | -0.68. 0.30 | -0.46 | -1.03-0.12 | 0.34 | -0.11-0.79 | -0.05 | -0.66-0.55 | |||||
| Rate of change | |||||||||||||
| PGS | -0.00 | -0.03-0.02 | -0.01 | -0.02-0.05 | 0.01 | -0.04-0.05 | 0.04 | -0.02-0.10 | |||||
| Higher degree | - | - | - | - | - | - | - | - | |||||
| Secondary qualification | 0.06** | 0.02–0.10 | 0.02 | -0.02-0.07 | 0.07 | 0.00-0.14 | 0.01 | -0.07-0.10 | |||||
| Primary qualification | 0.06** | 0.01–0.11 | 0.05 | -0.00-0.10 | 0.02 | -0.04-0.09 | -0.07 | -0.14-0.01 | |||||
| PGS × Higher degree | - | - | - | - | - | - | - | - | |||||
| PGS × Secondary qualification | 0.01 | -0.02-0.05 | -0.03 | -0.08-0.01 | 0.02 | -0.04-0.08 | -0.03 | -0.12-0.04 | |||||
| PGS × Primary qualification | 0.01 | -0.03-0.06 | 0.01 | -0.04-0.06 | -0.03 | -0.09-0.03 | -0.01 | -0.08-0.06 | |||||
| Variance a | |||||||||||||
| Within-person | 0.04 | 0.03–0.05 | 0.05 | 0.03–0.06 | 0.03 | 0.02–0.07 | 0.04 | 0.02–0.09 | |||||
| In initial status | 15.89 | 14.74–17.13 | 24.44 | 22.78–26.22 | 12.79 | 11.22–14.58 | 20.72 | 18.58–23.12 | |||||
| In rate of change | 0.03 | -0.06-0.11 | 0.01 | -0.11- 0.11 | -0.06 | -0.26-0.14 | 0.15 | -0.04-0.33 | |||||
CI, confidence intervals; PGS, polygenic score; BMI, body mass index
The adjusted models were adjusted for 4 principal components to account for any ancestry differences in genetic structures that could bias the results, as well as; marital status, physical activity level, presence of longstanding limiting illness, self-reported health, depressive symptoms, and smoking status.
a The within-person variance is the overall residual variance in cognition that is not explained by the model. The initial status variance component is the variance of individuals’ intercepts about the intercept of the average person. The rate of change variance component is the variance of individual slopes about the slope of the average person.
× represents an interaction between the two factors; interactions are presented based on multiplicative interaction model
***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05
As compared to the highest SSS tertile, being in the middle tertile off SSS (β = 0.75; 95%CI = 0.20–1.29) and bottom tertile of SSS (β = 1.16; 95%CI = 0.08–2.24) was associated with a higher BMI at baseline for women aged ≤ 65 years old. However, for men aged ≤ 65 years old, being in the bottom tertile of SSS was associated with a lower BMI at baseline (β=-1.68; 95%CI=-2.55- -0.82) (Fig. 2). In both men and women aged > 65 years old, there was no association between SSS and baseline BMI (Table 3). There was an interaction effect between BMI-PGS and SSS on baseline BMI found for women aged ≤ 65 years old, such that a 1-SD increase in BMI-PGS was associated with lower baseline BMI of 1.41 points for women in the bottom tertile of SSS (β=-1.41; 95%CI=-2.46- -0.36) (Table 3). There were two interaction effects found for change in BMI over time. A Higher BMI-PGS was associated with a reduction in BMI across time for men (aged ≤ 65) in the bottom tertile of SSS as compared to the highest SSS tertile (β =-0.09; 95%CI= -0.17- -0.01) (Table 3). While for women aged > 65 years old, the BMI-PGS was associated with reductions in BMI for those in the bottom tertile of SSS (β=-0.16; 95%CI=-0.32- -0.01).
| ≤ 65 Years of Age | > 65 Years of Age | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Men | Women | Men | Women | ||||||||||
| β | 95% CI | β | 95% CI | β | 95% CI | β | 95% CI | ||||||
| Baseline | |||||||||||||
| PGS | 1.28*** | 0.87–1.68 | 1.54*** | 1.01–2.01 | 0.52* | 0.10–0.94 | 1.23*** | 0.61–1.84 | |||||
| Top tertile | - | - | - | - | - | - | - | - | |||||
| Middle tertile | -0.33 | -0.81-0.15 | 0.75* | 0.20–1.29 | 0.05 | -0.49-0.60 | 0.37 | -0.35-1.09 | |||||
| Bottom tertile | -1.68*** | -2.55- -0.82 | 1.16* | 0.08–2.24 | 0.44 | -0.45-1.35 | 0.37 | -0.74-1.47 | |||||
| PGS × Top tertile | - | - | - | - | - | - | - | - | |||||
| PGS × Middle tertile | -0.02 | -0.49-0.45 | 0.01 | -0.45-0.62 | 0.42 | -0.06-0.92 | -0.06 | -0.75-0.62 | |||||
| PGS × Bottom tertile | 0.41 | -0.44-1.26 | -1.41** | -2.46- -0.36 | 0.64 | -0.45-1.41 | 0.20 | -0.91-1.31 | |||||
| Rate of change | |||||||||||||
| PGS | 0.03 | -0.01-0.07 | 0.01 | -0.03-0.05 | 0.03 | -0.00-0.08 | 0.08 | -0.02-.18 | |||||
| Top tertile | - | - | - | - | - | - | - | - | |||||
| Middle tertile | 0.03 | -0.02-0.08 | -0.02 | -0.07-0.03 | -0.03 | -0.09-0.03 | 0.01 | -0.09-0.11 | |||||
| Bottom tertile | 0.02 | -0.08-0.11 | 0.03 | -0.06-0.12 | -0.01 | -0.14-0.12 | -0.08 | -0.24-0.07 | |||||
| PGS × Top tertile | - | - | - | - | - | - | - | - | |||||
| PGS × Middle tertile | -0.03 | -0.07-0.01 | 0.00 | -0.04-0.05 | -0.04 | -0.08-0.01 | -0.06 | -0.17 -0.04 | |||||
| PGS × Bottom tertile | -0.09* | -0.17- -0.01 | -0.01 | -0.12-0.10 | -0.01 | -0.10-0.09 | -0.16* | -0.33- -0.07 | |||||
| Variance a | |||||||||||||
| Within-person | 0.05 | 0.03–0.06 | 0.05 | 0.03–0.06 | 0.03 | 0.02–0.07 | 0.04 | 0.03–0.09 | |||||
| In initial status | 15.80 | 14.27–17.50 | 25.11 | 22.92–27.49 | 12.93 | 11.34–14.73 | 21.14 | 19.05–23.45 | |||||
| In rate of change | 0.05 | -0.10-0.20 | -0.03 | -0.18-0.12 | -0.06 | -0.26-0.13 | 0.13 | -0.06-0.32 | |||||
CI, confidence intervals; PGS, polygenic score; BMI, body mass index
The adjusted models were adjusted for 4 principal components to account for any ancestry differences in genetic structures that could bias the results, as well as; marital status, physical activity level, presence of longstanding limiting illness, self-reported health, depressive symptoms, and smoking status. Adjusted models used robust standard errors to relax the assumption that standard errors carried identical and equal distributions, due to the presence of heteroscedascity.
a The within-person variance is the overall residual variance in cognition that is not explained by the model. The initial status variance component is the variance of individuals’ intercepts about the intercept of the average person. The rate of change variance component is the variance of individual slopes about the slope of the average person.
× represents an interaction between the two factors; interactions are presented based on multiplicative interaction model
***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05
Compared with the highest income tertile, women who were aged > 65 years old in the intermediate (β = 0.81, 95%CI = 0.09–1.53) and lowest income tertiles groups (β = 0.86, 95%CI = 0.18- -1.53) had higher baseline BMI values (Table 4). While for men aged > 65 years old, the intermediate income tertile showed lower baseline BMI values (β=-0.67, 95%CI=-1.21- -0.13). There was an interaction effect between BMI-PGS and income for BMI at baseline for men aged ≤ 65, such that a 1-SD increase in BMI-PGS was associated with a lower baseline BMI value of 0.72 points for men in the lowest tertile of income but not in the highest (β=-0.72, 95%CI=-1.21- -0.23) (Table 4). There were no significant direct effects or interaction effects of income on the rate of change in BMI over the 12-year follow up for either men or women.
| ≤ 65 Years of Age | > 65 Years of Age | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Men | Women | Men | Women | ||||||||
| β | 95% CI | β | 95% CI | β | 95% CI | β | 95% CI | ||||
| Baseline | |||||||||||
| PGS | 1.41*** | 1.12–1.69 | 1.52*** | 1.17–1.85 | 0.85*** | 0.47–1.22 | 1.33*** | 0.75–1.91 | |||
| High income | - | - | - | - | - | - | |||||
| Intermediate income | 0.09 | -0.36-0.56 | 0.51 | -0.04-1.06 | -0.67* | -1.21- -0.13 | 0.81* | 0.09–1.53 | |||
| Low income | -0.39 | -0.91-0.13 | 0.55 | -0.02- 1.13 | -0.35 | -0.87-0.18 | 0.86* | 0.18–1.48 | |||
| PGS × High income | - | - | - | - | - | - | - | - | |||
| PGS × Intermediate income | 0.17 | -0.29-0.63 | 0.14 | -0.41-0.69 | -0.09 | -0.61-0.43 | -0.24 | -1.01-0.51 | |||
| PGS × Low income | -0.72** | -1.21- -0.23 | -0.22 | -0.78-0.33 | 0.22 | -0.28-0.73 | -0.17 | -0.84-0.50 | |||
| Rate of change | |||||||||||
| PGS | -0.00 | -0.02-0.02 | 0.01 | -0.01-0.04 | 0.01 | -0.02-0.05 | -0.02 | -0.09-0.06 | |||
| High income | - | - | - | -- | - | - | - | - | |||
| Intermediate income | 0.03 | -0.01-0.08 | 0.03 | -0.02-0.07 | 0.05 | -0.01-0.12 | -0.03 | -0.08-0.08 | |||
| Low income | 0.03 | -0.02-0.08 | 0.03 | -0.01-.08 | 0.06 | -0.00-0.12 | -0.04 | -0.12-0.04 | |||
| PGS × High income | - | - | - | - | - | - | |||||
| PGS × Intermediate income | 0.00 | -0.04-0.04 | -0.04 | -0.08-0.01 | 0.01 | -0.06-0.07 | 0.06 | -0.03-0.15 | |||
| PGS × Low income | 0.01 | -0.03-0.06 | 0.02 | -0.02-0.08 | -0.03 | -0.09-0.03 | 0.04 | -0.05-0.13 | |||
| Variance a | |||||||||||
| Within-person | 0.04 | 0.03–0.05 | 0.05 | .04-0.06 | 0.03 | 0.02–0.07 | 0.04 | 0.02–0.08 | |||
| In initial status | 16.02 | 14.88–17.25 | 25.22 | 23.57–26.99 | 13.59 | 11.33–14.67 | 20.95 | 18.95–23.16 | |||
| In rate of change | 0.06 | -0.02-.14 | -0.04 | -0.15-0.07 | 0.07 | -0.02-0.15 | -0.02 | -0.12-0.08 | |||
| CI, confidence intervals; PGS, polygenic score; BMI, body mass index | |||||||||||
The adjusted models were adjusted for 4 principal components to account for any ancestry differences in genetic structures that could bias the results, as well as; marital status, physical activity level, presence of longstanding limiting illness, self-reported health, depressive symptoms, and smoking status.
a The within-person variance is the overall residual variance in cognition that is not explained by the model. The initial status variance component is the variance of individuals’ intercepts about the intercept of the average person. The rate of change variance component is the variance of individual slopes about the slope of the average person.
× represents an interaction between the two factors; interactions are presented based on multiplicative interaction model
***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05
Upon imputation, we observed that as compared to a higher qualification, men with a primary level education had a higher BMI at baseline in both the > 65 age group (β = 0.51; 95%CI = 0.08–1.11) and ≤ 65 age group (β = 0.49; 95%CI = 0.00–0.98) (Supplementary Table 3). For SSS, the interaction between BMI-PGS and the bottom tertile of SSS on rate of change in BMI in men aged > 65 uncovered in the main analyses was attenuated towards null. Moreover, women aged ≤ 65 and in the lowest tertile of SSS, no longer showed higher BMI at baseline (Supplementary Table 4). For income, women aged ≤ 65 and in the mid and lower income tertiles did not show higher BMI at baseline as in the main analyses (Supplementary Table 5).
To our knowledge, this is the first study to investigate GxE between an aggregate measure of genetic risk for BMI and three dimensions of socioeconomic position the rate of change in BMI across older adulthood. Consistent with previous findings14,36, our results showed that a higher BMI-PGS was associated with higher baseline BMI (~ 64 years of age) in both men and women. However, contrary to our second hypothesis, BMI-PGS was not significantly associated with a rate of change in BMI during the 12-year follow-up period. These results may imply that polygenic factors that contribute to BMI variation in mid-to-older adulthood may differ from those which influence BMI fluctuation into older age.
Consistent with previous findings, our results demonstrate that lower levels of educational attainment, SSS and income were associated with higher baseline BMI more so in women than in men.37 Moreover, males with lower SSS and incomes showed lower BMI values at baseline. These gender differences may represent how women in mid-to-late adulthood may be more exposed to the reduced opportunities for physical activity and lower quality diet brought about by lower SEP settings than men, and therefore show a stronger social gradient in BMI outcomes than men of the same social standing. On the other hand, a reverse causal effect between obesity and labour market outcomes may be present, such that developing obesity earlier in life might influence a women’s labour market outcomes, and hence income and SSS, to a greater extent than men. A novel finding was also that SSS was only associated with baseline BMI in adults aged 65 and younger. It has been proposed that those who perceive themselves to be of lower resources may be more exposed and more susceptible to the obesogenic environment.19 Hence, as younger populations have developed in the context of a more obesogenic environment, the influence of self-perceived resources might therefore be stronger in younger age groups.19 This finding highlights how BMI inequalities may vary across specific age ranges, and future investigations may benefit from exploring social gradients across both gender, SEP measures, and stages of the life course.
Three GxE interactions between socioeconomic positioning and BMI-PGS were observed. First, the BMI-PGS showed a stronger association with baseline BMI in men (aged ≤ 65) with a secondary qualification than those with a higher qualification. Hence, a lower educational attainment may accentuate genetic risk for BMI as less education may place individuals within more obesogenic environments where opportunities to express underlying genetic risk are more pervasive. 16,20 We further observed that in men of a lower income (aged ≤ 65) higher BMI-PGS scores were associated with baseline BMI values, as compared to those in a higher income group. Finally, for women aged ≤ 65 or younger, a higher BMI-PGS was associated with a lower baseline BMI only for those in the lowest SSS tertile. Together, these findings might suggest that women’s expression of polygenic risk towards a higher BMI is more influenced by subjective measures of social standing than tangible levels of education or income. Nonetheless, while these findings provide evidence that the expression of polygenic predisposition may be sensitive to the socioeconomic environment, it is noteworthy that, similar Tyrell et al20, the present GxE interactions produced smaller effect sizes than the direct effects of socioeconomic status on BMI.
In the present study, we analysed a large population-based cohort who are representative of older adults in England. Confidence in these findings is also strengthened by using LMMs, which are an optimal way to describe the changes in continuous dependent variables over time taking into account intra and inter-individual variation. Moreover, the sample utilised in the present study was deemed appropriate for the stated hypotheses as it was substantially larger or similar to samples of previous work.18,19
Nonetheless, given the observational nature of this study, we cannot infer causality or eliminate the role of residual confounding. It is feasible that PGS utilised in the present study, having encompassed hundreds to thousands of common variants, may have accumulated noise which masks the true associations with changes in BMI over time.15 Moreover, the poor generalizability of genetic studies across populations is noteworthy as PGSs are predominately based on in European participants.30 Moreover, as GWASs do not, by design, capture other structural variants beyond SNPs such as rare variants, poorly tagged or multiple independent variants, G×G interaction, epigenetics and gene-environment correlation.40 Moreover, to avoid overfitting the present GxE models we also were unable to adjust our analyses for interactions between the covariates and the present BMI-PGS and SEP variables, as advised by Keller et al.40 Finally, the use of height data from Wave S6 to calculate BMI at wave 8 may have affected the validity of the final follow-up BMI measures.
The BMI-PGS was associated with higher BMI at baseline but not with the rate of change in BMI over the 12-year follow-up period. Moreover, women’s BMI appeared to be more adversely affected by lower education, SSS, and income than men’s in mid to late adulthood. Crucially, the current results highlight the potential for educational attainment, SSS, and income to influence BMI in adulthood through interaction with a BMI-PGS, although effect sizes were small. Taken together, lower socioeconomic positioning may adversely influence BMI in adulthood both independently and through accentuation of genetic risk. However, further research must clarify the extent to which cumulative measures of socioeconomic conditions may influence the expression of genetic propensity towards a higher BMI.
Data availability statement: The ELSA data are available in public, open-access repository (the UK Data Archive) which is freely available and can be accessed at https://discover.ukdataservice.ac.uk.
Funding/Support: The English Longitudinal Study of Ageing is funded by the National Institute on Aging (grant RO1AG7644) and by a consortium of UK government departments coordinated by the Economic and Social Research Council (ESRC) and the National Institute for Health Research (NIHR). OA is jointly funded by an NIHR Post-Doctoral Fellowship (PDF-2018-11-ST2-020). KT is funded by the ESRC-BBSRC Soc-B Centre for Doctoral Training (grant number: ES/P000347/1).
Competing Interest Statement: The authors declare no competing financial interests.
Author Contributions: All authors were involved in the conceptualization of the study. KT led on undertaking the quantitative analyses and drafting the initial manuscript. OA contributed to the design of the statistical analysis, interpretation of the results, and reviewed and revised the manuscript multiple times. AS provided feedback on the design of the analysis, the interpretation of the results, and revised the manuscript.